Downtimes during valuable operating time on the store floor - the MES is sounding the alarm! It wants to draw attention to a reduction in Overall Equipment Efficiency (OEE). In such cases, the blame is often placed solely on the workers or the production systems. However, it may be that everything has been done correctly in these sub-processes and the problems have occurred elsewhere, for example in the provision of materials or in other sub-processes of the company organization. The true cause is only recognized late, often too late or in many cases not at all.

This is precisely where process mining comes in, combining process-oriented business process management with non-process-oriented data mining. Compared to data mining, process mining has the advantage of being able to assign and visualize the raw data collected to specific (sub)processes. This makes it possible to monitor and improve the overall process in its entire granularity. Particularly with an increasing degree of automation and increasing correlation of previously autonomous individual processes, downtimes can be identified through the precise reconstruction of dependencies. Cause-and-effect relationships can thus be tracked down without having to get lost in lengthy analyses. Once the correlations have been identified, the collected data can be further analyzed using process mining in conjunction with data mining methods in order to derive decision rules, for example.

CIP - on the way to the digital twin of production

Process mining supports the continuous improvement process (CIP), the supreme discipline in production. If you look at the classic PDCA cycle (plan-do-check-act), you will notice that an important phase is missing; however, it is assumed in the models. The "realize" phase is the section in which the PDCA cycle actually begins. In this phase, Proxia Software AG's MES offers the right tools to detect deviations and identify problems in the processes. In the "check" phase, additional tools such as the Proxia Action Manager are available. It can be used to examine implemented measures for specific targets or changes in status. For example, the change in the downtime pattern of systems can be examined after the material provision process has been adjusted. This means that the question of whether switching to a supermarket concept with pre-picking has a positive effect on the productivity of downstream processes can be answered with a reliable database. At the same time, the impact on the overall order throughput time can be evaluated. By checking various key figures in the context of a measure, potential conflicts of objectives can be identified. A completed measure remains in the Proxia system as a digital image. This makes it possible to repeatedly check the quality of measures once they have been implemented.

Location of process mining - ERP or MES?

While the ERP system is used to monitor and control the type and quantity of orders for a period of time, Proxia MES is used to determine processing times, allocation of resources and the order processing sequence in production. In this system symbiosis, the ERP system is usually referred to as the "leading system". This statement is also true for the management of master data and the holistic control of the value creation process. However, many ERP systems do not sufficiently immerse themselves in the "microcosm" of production.

At this point, Proxia's MES takes over the operational process and ensures agile and optimized control of the value stream. In addition, the MES supports the ERP systems in controlling the higher-level value creation process through a permanent flow of information. This results in a clear separation of responsibilities and tasks. Proxia's MES functional scope includes production planning, process planning, order control, machine data/operating data acquisition, maintenance control and quality management - as well as the provision of data mining functionalities. Generally speaking, data mining promotes (non-process-related) correlations from mass data by identifying new cross-connections and trends based on data. This is where Proxia Software's reporting module provides support. Depending on how long the system has been digitally tracking the production processes, representative data sections can be used for the analysis. For example, times or events at which parameters have changed can be determined. By capturing and recording the data from production in its entirety, the causes of these events can now be determined.

Many current ERP installations are only suitable for the detailed management of dynamic production processes to a limited extent. This is due to the data model alone, as ERP systems are often aimed at managing costs and materials and are used to allocate personnel, material and overhead costs. Modelling complex dynamic processes and visualizing them in a user-friendly way is not one of their tasks. In contrast, the Proxia Timeline analysis tool has a completely different focus: here, the entire production process is displayed graphically. This means that a deviation can be recognized at first glance without having to study comprehensive figures. Even more complex questions such as: "Did the transfer of partial quantities within production work smoothly?" "How is the image of my ghost shift?" are answered graphically. This relieves the administrative workload in production because less time has to be spent on production controlling.

6σ-Philosophy completely digitized

6σ aims to reduce quality defects within defined specification limits of very few events per 1 million possibilities. In contrast to other quality improvement methods, the focus is on information-driven improvement, which is expressed in the so-called DMAIC cycle: Define, Measure, Analyze, Improve, Control. This involves defining areas of application, collecting ("measuring") the relevant data, analyzing the causes of problems, improving performance in the problem areas and finally controlling them. This applies to the end-to-end process as well as to subsections of it. It is therefore clear that the implementation of 6σ depends heavily on the availability, collection, analysis and application of information obtained from the process data. Due to this dependence on reliable data, the MES is an important building block for a successful 6σ implementation.

This makes it easy to bridge the gap between process mining and a 6σ project. Process mining helps to significantly increase and accelerate the efficiency of a 6σ project, for example through Proxia's process mining tools. At this point, both classic analysis tools such as the Proxia Timeline and the Measure Manager are used, with the Timeline processes can be visualized. Process-related parameters such as temperatures, pressures or speeds are displayed synchronously with the process. With the Measures Manager, it is possible to examine the change in process parameters and key figures in relation to process changes.